Abstract 1497: miRNA expression analysis in high-risk HPV-positive cervical scrapes for the detection of cervical disease
Authors: Martha Isabel González Ramírez, Samuel Agudelo, Maria Cecilia Agudelo, Jone Garai, Li Li, Carlos Alberto Orozco Castaño, Jovanny Zabaleta, Gloria Inés Sánchez Vásquez
Journal: Cancer Research (2022)

Abstract Cervical cancer is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer death in women, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020. Persistent infections with high-risk HPV (hrHPV) genotypes can lead to high-grade lesions (Cervical Intraepithelial Neoplasia Grade 2 or higher, CIN2+), that if left untreated progress to cancer. hrHPV test has high sensitivity to detect CIN2+, but it has low specificity because close to 90% of women spontaneously clear the infection. Biomarkers to stratify hrHPV+ women with cervical lesions that may progress to cancer are needed. miRNAs are small noncoding RNAs that regulate gene expression, can be detected in cervical scrapes, and are differentially expressed between high- and low-grade lesions. Aim To identify miRNAs differentially expressed between CIN3+ and ≤CIN1 lesions and evaluate their potential use as biomarkers to distinguish CIN3+ in hrHPV+ women. Methods We used miRNAseq to compare miRNAs expression patterns in cervical scrapes of hrHPV+ women: 35 with low-grade lesions (NEG= 26; CIN1= 9) and 36 with high-grade lesions (CIN3= 32; SCC= 4). The samples were collected through the ASCUS-COL Trial in Medellin, Colombia. Women with low- or high-grade cervical lesions exhibit similar sociodemographic characteristics (Chi-square p-values&amp;gt;0.05). The RNAseq data were processed in GeneGlobe-QIAGEN, which incorporates cut-adapt, bowtie and DeSEq2. Receiver Operating Characteristic (ROC) analyses with a 95% confidence interval of Area Under the Curve (AUC) were made to evaluate the diagnostic accuracy of each miRNA differentially expressed, to detect CIN3+. Multivariate Logistic Regression Analysis using normalized counts of mapped reads identified a combination of the differentially expressed miRNAs that best predicted CIN3+. We identified putative pathways using MetaCore. Results An average of &amp;gt;9 million reads by sample and around 3.5 million reads mapped to miRBase V21 was obtained. The principal component analysis did not show factors that could introduce bias to differential gene expression analysis. We identified 38 miRNAs differentially expressed. Compared to &amp;lt;CIN1 lesions, 9 miRNAs were overexpressed and 29 underexpressed in CIN3+ lesions. Six miRNAs presented AUC&amp;gt;0.60 (p-value &amp;lt;0.05) to detect CIN3+. The best predictive combination of 9 miRNAs exhibits an AUC of 0.89, 95% CI (0.83 - 0.97), of which 4 were overexpressed and 5 underexpressed in CIN3+ vs &amp;lt;CIN1. Interestingly, 5 miRNAs underexpressed in CIN3+, target VEGF, a known angiogenic mediator linked to malignancy. Conclusion We identified miRNAs differentially expressed with good diagnostic performance to distinguish high- from low-grade lesions in cervical scrapes samples. Further validation on a larger cohort of cervical scrapes samples is needed to confirm the potential role of these miRNAs to triage hrHPV+ women. Citation Format: Martha Isabel González Ramírez, Samuel Agudelo, Maria Cecilia Agudelo, Jone Garai, Li Li, Carlos Alberto Orozco Castaño, Jovanny Zabaleta, Gloria Inés Sánchez Vásquez. miRNA expression analysis in high-risk HPV-positive cervical scrapes for the detection of cervical disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1497.